There are a variety of software packages available for analysis of functional MRI (fMRI) data. Widespread use of a thoroughly tested package is advantageous not only because of the ready availability to research labs without extensive computer programming resources, but also is a vehicle to facilitate comparisons and reproducibility among different research sites. We have experience with the following packages in our lab: AFNI, BrainVoyager, MEDx, SPM99, and VoxBo. Each of these packages is used by several laboratories around the world, and is generally accepted by the research community. Furthermore, each of these packages has a similar general approach to fMRI analysis, consisting of pre-processing (motion correction, coregistration to a template), parametric voxelwise comparison with a time-varying activation paradigm, definition of a statistical model, and interrogation and display of statistically meaningful results. However, the implementation of these basic steps varies considerably from one package to another, and many users find it difficult to select the best package for a given application based on criteria other than simply having started out using one package and becoming familiar with it. Each package has a different approach to preprocessing, specifying and estimating a statistical model, corrections for multiple comparisons, and to interrogation of intermediate and final results. Other considerations include the ease of learning and using a package, the availability and complexity of a Graphical User Interface (GUI), the ability to employ scripts to batch-process large analysis efforts, and the computational time required to perform the various analytic steps. Of course, the most important criteria is the accuracy, sensitivity, and specificity of the results with respect to a range of BOLD signal changes; while each of the above packages has an acceptable level of accuracy and sensitivity, how these levels compare across the packages has not been thoroughly investigated. We propose to perform a series of well-characterized fMRI scans, representative of research efforts in a variety of neuroimaging applications. We will use these scans to investigate the utility and effectiveness of five common fMRI analysis packages. We will also construct several series of synthetic data with simulated activations of varying magnitude and extent, which we will use to quantify the accuracy of motion correction and spatial normalization, and the ability of each package to detect small activations. Given the large amount of energy expended on neuroimaging experiments and the importance of knowledge to be gained, it is vital for researchers to select the best tools to reduce and analyze these complex data.